Soft tissue material cumulative damage model for reducing repetitive stress injuries in performing a process

Abstract

Techniques for reducing repetitive stress injuries to soft tissue in performing a process are disclosed. The techniques include obtaining at least one repetitive stress data set related to the soft tissue and to the process; accessing information characterizing a first damage regime and second information characterizing a second damage regime, where the first information quantifies a number of repetitions at a given stress for the soft tissue to transition out of the first damage regime, and wherein the second information quantifies a number of repetitions at a given stress for the soft tissue to transition out of the second damage regime; predicting conditions sufficient for damage to the soft tissue; determining, based on at least the predicting, at least one guideline for reducing a risk of a soft tissue material repetitive stress injury; and implementing the at least one guideline in the process.

What is claimed is: 
     
         1 . A method of reducing the potential for repetitive stress injuries to human soft tissue in performing an assembly line manufacturing process, the method comprising:
 obtaining at least one repetitive stress data set related to the soft tissue and to the assembly line manufacturing process, wherein the repetitive stress data set characterizes an exposure that a worker accrues in completing an assembly line task that forms part of the assembly line manufacturing process;   accessing at least information characterizing a first damage regime, wherein the first damage regime consists of a no damage regime, wherein the information quantifies a number of repetitions at a given stress for the soft tissue to transition out of the first damage regime;   predicting, based on at least the information and the repetitive stress data set, conditions sufficient for damage to the soft tissue, wherein the predicting comprises predicting conditions sufficient to transition from the first damage regime to a second damage regime, wherein the second damage regime consists of a sub-rupture damage regime;   determining, based on at least the predicting, at least one guideline for reducing a risk of a soft tissue material repetitive stress injury, wherein the at least one guideline specifies a limitation on a number of repetitions of a given movement of a supraspinatus tendon, wherein the at least one guideline is for reducing a risk of a supraspinatus tendon repetitive stress injury, wherein the determining comprises determining at least one guideline for reducing a risk of transitioning from the first damage regime to the second damage regime; and   implementing the at least one guideline in the assembly line manufacturing process, wherein the implementing comprises modifying the assembly line task of the worker in conformance with the at least one guideline, and wherein the implementing comprises the worker performing the modified assembly line task in conformance with the at least one guideline.   
     
     
         2 . The method of  claim 1 , wherein the repetitive stress data set comprises a force over a cross sectional area of the soft tissue and at least one of a number of repetitions for, or a duration of, at least one task of the process. 
     
     
         3 . The method of  claim 1 , wherein the obtaining the at least one repetitive stress data set related to the soft tissue and to the process comprises estimating at least one stress distribution in the soft tissue. 
     
     
         4 . The method of  claim 1 , wherein the accessing at least information characterizing a first damage regime comprises obtaining at least one of: ultrasound data, computerized axial tomography (CAT) scan data, magnetic resonance imaging (MRI) scan data, destructive testing data, cadaver material data, animal material data, polymer surrogate material data, molecular dynamic modeling (MDM) data, or publication data. 
     
     
         5 . The method of  claim 1 , wherein the soft tissue further comprises a teres minor tendon, an infraspinatus tendon, a subscapularis tendon, a deltoid tendons, a biceps tendon, a triceps tendon, a brachioradialis tendon, a supinator tendon, a flexor carpi radialis tendon, a flexor carpi ulnaris tendon, an extensor carpi radialis tendon, an extensor carpi radialis brevis tendon, an iliopsoas tendon, an obturator internus tendon, an adductor longus tendon, an adductor brevis tendon, an adductor magnus tendon, a gluteus maximus tendon, a gluteus medius tendon, a quadriceps tendon, a patellar tendon, a hamstring tendon, a sartorius tendon, a gastrocnemius tendon, an Achilles tendon, a soleus tendon, a tibialis anterior tendon, a peroneus longus tendon, a flexor digitorum longus tendon, an interosseus tendon, a flexor digitorum profundus tendon, an abductor digiti minimi tendon, an opponens pollicis tendon, a flexor pollicis longus tendon, an extensor tendon, an abductor pollicis tendon, a flexor hallucis longus tendon, a flexor digitorum brevis tendon, a lumbrical tendon, an abductor hallucis tendon, a flexor digitorum longus tendon, an abductor digiti minimi tendon, an ocular tendon, a levator palpebrae tendon, a masseter tendon, a temporalis tendon, a trapezius tendon, a sternocleidomastoideon, a semispinalis capitis tendon, a splenius capitis tendon, a mylohyoid tendon, a thyrohyoid tendon, a sternohyoid tendon, a rectus abdominis tendon, an external oblique tendon, a transversus abdominis tendon, a latissimus dorsi tendon, or an erector spinae tendon. 
     
     
         6 . The method of  claim 1 , further comprising individualizing the at least one guideline for a subject by applying one or more demographic variables for the subject to the at least one guideline. 
     
     
         7 . The method of  claim 1 , further comprising:
 obtaining one or more usage data sets for the soft tissue in a subject; and   estimating damage to the soft tissue in the subject by comparing the usage data sets to the at least one guideline.   
     
     
         8 . A computer system for reducing repetitive stress injuries to soft tissue in performing an assembly line manufacturing process, the system comprising at least one electronic processor that executes instructions to perform operations comprising:
 obtaining at least one repetitive stress data set related to the soft tissue and to the assembly line manufacturing process, wherein the repetitive stress data set characterizes an exposure that a worker accrues in completing an assembly line task that forms part of the assembly line manufacturing process;   accessing at least information characterizing a first damage regime, wherein the first damage regime consists of a no damage regime, wherein the information quantifies a number of repetitions at a given stress for the soft tissue to transition out of the first damage regime;   predicting, based on at least the information and the repetitive stress data set, conditions sufficient for damage to the soft tissue, wherein the predicting comprises predicting conditions sufficient to transition from the first damage regime to a second damage regime, wherein the second damage regime consists of a sub-rupture damage regime; and   determining, based on at least the predicting, at least one guideline for reducing a risk of a soft tissue material repetitive stress injury, wherein the at least one guideline specifies a limitation on a number of repetitions of a given movement of a supraspinatus tendon, wherein the at least one guideline is for reducing a risk of a supraspinatus tendon repetitive stress injury, wherein the determining comprises determining at least one guideline for reducing a risk of transitioning from the first damage regime to the second damage regime;   whereby the at least one guideline is implemented in the assembly line manufacturing process, wherein the implementing comprises modifying the assembly line task of the worker in conformance with the at least one guideline, and wherein the guideline is implemented by the worker performing the modified assembly line task in conformance with the at least one guideline.   
     
     
         9 . The system of  claim 8 , wherein the repetitive stress data set comprises a force over a cross sectional area of the soft tissue and at least one of a number of repetitions for, or a duration of, at least one task of the process. 
     
     
         10 . The system of  claim 8 , wherein the obtaining the at least one repetitive stress data set related to the soft tissue and to the process comprises estimating at least one stress distribution in the soft tissue. 
     
     
         11 . The system of  claim 8 , wherein the accessing at least information characterizing a first damage regime comprises obtaining at least one of: ultrasound data, computerized axial tomography (CAT) scan data, magnetic resonance imaging (MRI) scan data, destructive testing data, cadaver material data, animal material data, polymer surrogate material data, molecular dynamic modeling (MDM) data, or publication data. 
     
     
         12 . The system of  claim 8 , wherein the soft tissue further comprises a teres minor tendon, an infraspinatus tendon, a subscapularis tendon, a deltoid tendons, a biceps tendon, a triceps tendon, a brachioradialis tendon, a supinator tendon, a flexor carpi radialis tendon, a flexor carpi ulnaris tendon, an extensor carpi radialis tendon, an extensor carpi radialis brevis tendon, an iliopsoas tendon, an obturator internus tendon, an adductor longus tendon, an adductor brevis tendon, an adductor magnus tendon, a gluteus maximus tendon, a gluteus medius tendon, a quadriceps tendon, a patellar tendon, a hamstring tendon, a sartorius tendon, a gastrocnemius tendon, an Achilles tendon, a soleus tendon, a tibialis anterior tendon, a peroneus longus tendon, a flexor digitorum longus tendon, an interosseus tendon, a flexor digitorum profundus tendon, an abductor digiti minimi tendon, an opponens pollicis tendon, a flexor pollicis longus tendon, an extensor tendon, an abductor pollicis tendon, a flexor hallucis longus tendon, a flexor digitorum brevis tendon, a lumbrical tendon, an abductor hallucis tendon, a flexor digitorum longus tendon, an abductor digiti minimi tendon, an ocular tendon, a levator palpebrae tendon, a masseter tendon, a temporalis tendon, a trapezius tendon, a sternocleidomastoideon, a semispinalis capitis tendon, a splenius capitis tendon, a mylohyoid tendon, a thyrohyoid tendon, a sternohyoid tendon, a rectus abdominis tendon, an external oblique tendon, a transversus abdominis tendon, a latissimus dorsi tendon, or an erector spinae tendon. 
     
     
         13 . The system of  claim 8 , wherein the operations further comprise individualizing the at least one guideline for a subject by applying one or more demographic variables for the subject to the at least one guideline. 
     
     
         14 . The system of  claim 8 , wherein the operations further comprise:
 obtaining one or more usage data sets for the soft tissue in a subject; and   estimating damage to the soft tissue in the subject by comparing the usage data sets to the at least one guideline.   
     
     
         15 . A method of reducing the potential for repetitive stress injuries to human soft tissue in performing an assembly line manufacturing process, the method comprising:
 obtaining at least one repetitive stress data set related to the soft tissue and to the assembly line manufacturing process, wherein the repetitive stress data set characterizes an exposure that a worker accrues in completing an assembly line task that forms part of the assembly line manufacturing process;   accessing at least information characterizing a first damage regime, wherein the first damage regime consists of a sub-rupture damage regime, wherein the information quantifies a number of repetitions at a given stress for the soft tissue to transition out of the first damage regime;   predicting, based on at least the information and the repetitive stress data set, conditions sufficient for damage to the soft tissue, wherein the predicting comprises predicting conditions sufficient to transition from the first damage regime to a second damage regime, wherein the second damage regime consists of a tear propagation regime;   determining, based on at least the predicting, at least one guideline for reducing a risk of a soft tissue material repetitive stress injury, wherein the at least one guideline specifies a limitation on a number of repetitions of a given movement of a supraspinatus tendon, wherein the at least one guideline is for reducing a risk of a supraspinatus tendon repetitive stress injury, wherein the determining comprises determining at least one guideline for reducing a risk of transitioning from the first damage regime to the second damage regime; and   implementing the at least one guideline in the assembly line manufacturing process, wherein the implementing comprises modifying the assembly line task of the worker in conformance with the at least one guideline, and wherein the implementing comprises the worker performing the modified assembly line task in conformance with the at least one guideline.   
     
     
         16 . The method of  claim 15 , wherein the accessing at least information characterizing a first damage regime comprises obtaining at least one of: ultrasound data, computerized axial tomography (CAT) scan data, magnetic resonance imaging (MRI) scan data, destructive testing data, cadaver material data, animal material data, polymer surrogate material data, molecular dynamic modeling (MDM) data, or publication data. 
     
     
         17 . The method of  claim 15 , wherein the obtaining the at least one repetitive stress data set related to the soft tissue and to the process comprises estimating at least one stress distribution in the soft tissue. 
     
     
         18 . A computer system for reducing repetitive stress injuries to soft tissue in performing an assembly line manufacturing process, the system comprising at least one electronic processor that executes instructions to perform operations comprising:
 obtaining at least one repetitive stress data set related to the soft tissue and to the assembly line manufacturing process, wherein the repetitive stress data set characterizes an exposure that a worker accrues in completing an assembly line task that forms part of the assembly line manufacturing process;   accessing at least information characterizing a first damage regime, wherein the first damage regime consists of a sub-rupture damage regime, wherein the information quantifies a number of repetitions at a given stress for the soft tissue to transition out of the first damage regime;   predicting, based on at least the information and the repetitive stress data set, conditions sufficient for damage to the soft tissue, wherein the predicting comprises predicting conditions sufficient to transition from the first damage regime to a second damage regime, wherein the second damage regime consists of a tear propagation regime;   determining, based on at least the predicting, at least one guideline for reducing a risk of a soft tissue material repetitive stress injury, wherein the at least one guideline specifies a limitation on a number of repetitions of a given movement of a supraspinatus tendon, wherein the at least one guideline is for reducing a risk of a supraspinatus tendon repetitive stress injury, wherein the determining comprises determining at least one guideline for reducing a risk of transitioning from the first damage regime to the second damage regime; and   implementing the at least one guideline in the assembly line manufacturing process, wherein the implementing comprises modifying the assembly line task of the worker in conformance with the at least one guideline, and wherein the implementing comprises the worker performing the modified assembly line task in conformance with the at least one guideline.   
     
     
         19 . The system of  claim 18 , wherein the accessing at least information characterizing a first damage regime comprises obtaining at least one of: ultrasound data, computerized axial tomography (CAT) scan data, magnetic resonance imaging (MRI) scan data, destructive testing data, cadaver material data, animal material data, polymer surrogate material data, molecular dynamic modeling (MDM) data, or publication data.